Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An image fusion process should preserve all useful patterns from the source images while minimizing artifacts that could interfere with subsequent analyses or distract human observers. Given that it is nearly impossible to fuse images without introducing some form of distortion, measurements are necessary to present a fused image quality (IQ) for user analysis. <strong>9.1 Introduction</strong> Image-quality measurement is as important as image fusion methods to guide developments for engineers, support learning methods for machines, and enhance trust with users. This chapter focuses on objective evaluation using quantitative metrics, whereas subjective evaluation will be discussed in Chapter 10. In order to objectively assess the performance of an image fusion method, a number of evaluation metrics, either objective or subjective, have been proposed. Studies on image fusion lack information that explicitly defines the applicability and feasibility of a specific fusion algorithm for a given application. Usually, a subjective evaluation is carried out to validate an objective assessment. However, identifying a reliable subjective score needs extensive experiments, which is expensive and cannot cover all possible conditions of interest. Typically, a robust performance model is required to account for the critical image fusion parameters and better assess the trend of image fusion performance quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it